6 New: Chordograms

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With the previous songs in mind, I have created key- and chordograms of top tracks from 2018 (Rosebud by U.S. Girls) and 2020 (Processed by the Boys by Protomartyr).

The level of prominence of each key/chord is represent by a darker colour.

I used “norm = manhattan; distance = manhattan” for both chordograms, and “norm = manhattan; distance = aitchison” for both keygrams.

Need to work out my findings

Things to consider: Compare above to top track of years 2016 (or other/more years).

1. Introduction: Diving into my 10-years worth of last.fm music log

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What is your corpus, why did you choose it, and what do you think is interesting about it?

Last.fm is an online music database, a music recommender system, and a social networking service combined, which was founded in the days when MSN, Myspace, and Runescape were still a thing. In general, the website offers a plugin for you to install on your PC and phone, which can track your listening behaviour. One listen is then transferred (“scrobbled”) to the database and displayed on your personal profile. With the accumulated data, they could also recommend you new/similar music to discover or connect you to people with similar music taste. Although the social aspects have been watered down, I’ve still been using their service ever since June 2011 (1). With a vast amount of data up for grabs, it would be a waste to leave the data as it is. That is why I’m interested in learning more about how my listening has changed over the years.

As of December 31st of 2020, I have approximately over 97.000 registered scrobbles over the course of ten years. The size is too big for the scope of this course, so I will be limiting to a set number of top tracks each year. This makes it easier to explore the data without losing much overview of my general listening behaviour.

What are the natural groups or comparison points in your corpus and what is expected between them? My corpus will be divided in years from 2011 (June) to the end of 2020.

How representative are the tracks in your corpus for the groups you want to compare? I used Spotlistr (2) to collect 60 tracks of each year from my last.fm profile. As I’ve been listening to albums rather than separate songs since 2015-2016, I decided to grab top 10 tracks and the remaining 50 tracks between #11-100 at random to broaden the scope. Sometimes the tool doesn’t pick the correct track (e.g. metadata changed), which I will then adjust manually. If a top 10 song is missing, then I’ll pick the next (#11 and so on).

Strengths and limitations

Identify several tracks in your corpus that are either extremely typical or atypical

References:

2. How much did I spend on listening to music?

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Things to do:

2014/2: “CE middelbare school” (correspond to lower valence as seen in next figures?)

Pre-2015: Only ‘scrobbles’ from PC/laptop.

2015/1: New phone and subscribed to Spotify with last.fm

2016/3: Start UvA

2018/3: Half year in Hong Kong

3.1 Mood features: From sad, sadder, saddest…

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3.2 Mood features: … to fast, loud, and noisy (relatively speaking).

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4. Investigating the scope of my outliers in chromagrams

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Outlier: Song’s very reliant on silence with occasional …

Things to consider: Most listened song vs favourite song of 2020. Compare multiple low valence/energy songs and see if they have something in common.

Optional: DTW of Dreams cover by Faye Wong

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Reasoning on inclusion: In top 2014. Might become an outtake if it doesn’t fit in my portfolio story.

5 Self-similarity Matrices

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On the left, you see two self-similarity matrices displaying chroma and timbre features of Processed by the Boys by Protomartyr (top track of 2020). As for the settings:segments are set in bars, applied normalization and summary statistics are euclidean and root mean square, respectively. The darker the colour brightness, the more similar the segments are compared to segments before it in time.

Findings:

Things to consider: Compare above to top track of years 2016, 2018 (Rosebud) or other/more years.

Conclusions

TODO

TODO